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  "Title": "Binary Causality Inference Framework",
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  "Authors@R": "person(given = \"Chainarong\",\nfamily = \"Amornbunchornvej\",\nrole = c(\"aut\", \"cre\"),\nemail = \"grandca@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3131-0370\"))",
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  "Description": "A framework to infer causality on binary data using\ntechniques in frequent pattern mining and estimation\nstatistics. Given a set of individual vectors S={x} where x(i)\nis a realization value of binary variable i, the framework\ninfers empirical causal relations of binary variables i,j from\nS in a form of causal graph G=(V,E) where V is a set of nodes\nrepresenting binary variables and there is an edge from i to j\nin E if the variable i causes j. The framework determines\ndependency among variables as well as analyzing confounding\nfactors before deciding whether i causes j.  The publication of\nthis package is at Chainarong Amornbunchornvej, Navaporn\nSurasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong\n(2023) <doi:10.1016/j.heliyon.2023.e15947>.",
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  "URL": "https://github.com/DarkEyes/BiCausality",
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    "VecAlignment"
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      "table": true,
      "tojson": true
    },
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      "rows": 200,
      "table": true,
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      "table": false,
      "tojson": false
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